The present invention relates to the field of text analysis and synthesis.
Decision making is a fundamental, yet often a challenging task in many fields. A key component in decision making is identifying arguments for and against each possible decision, in order to make an informed decision. Moreover, it is important for such arguments to be phrased in a coherent way, to clearly present a variety of arguments without overly delving into repetitive detail.
Related systems are those which deal with generation of reports in a natural language, based on structured data. Examples include CoGenTex's Forecast Generator (FoG) and Recommender, and Narrative Science's Quill for Google Analytics.
Text mining, also referred to as text analytics (or analysis), is often defined as the automated process of deriving high-quality information from text (specifically, large amounts of text) via computer processing. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning and machine learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. ‘High quality’ in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks may include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).
Text analysis may involve information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal may be, essentially, to turn text into data for analysis, via application of methods such as natural language processing (NLP) and analytical methods.
With continuous advancements and an increase in user popularity, data mining and text analysis technologies may serve as an invaluable resource across a wide range of disciplines.
The technology is now broadly applied for a wide variety of needs, including government, research and business needs. Applications of text analysis may include intelligence, security, e-discovery, records management, publishing, automated ad placement, social media monitoring, scientific discovery etc.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.